Network anomaly detection with the restricted Boltzmann machine

By Ugo Fiore, Francesco Palmieri, Aniello Castiglione, Alfredo De Santis. Neurocomputing, 2013

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/443b6e8fdb315d577b4f09f560b5a792d9745ad3

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In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

A Deep Learning Approach for Network Intrusion Detection System (2016, background): “It is envisioned that the deep learning based approaches can help to overcome the challenges of developing an eﬃcient NIDS [2, 5].”
Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “The three broad classes are [1], [13]: • Supervised anomaly detection : In this class, both the normal and anomalous training datasets contain labeled instances.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, methodology): “RBM [192,193] -R2L, DoS, U2R and Probe - Feedback function of RMBMs facilitates extraction of important attributes which are then used to capture the behavior of IoT traffic.”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “The DL models trained by KDD dataset may yield deteriorated performance in detecting real-world data, as shown in [113].”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, neutral): “[177] [178] [179] [180] [181] [182] [183] [184] [185] [186] [187] [188]”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “Semi-supervised [42],[43],[44],[45] CNN [55],[56]”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “The restricted Boltzmann machine was used for intrusion detection by Fiore et al. [47].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “To solve these challenges, the researchers in [221] proposed a learning model that is based on a discriminative RBM, which they selected due to its capability to combine generative models with suitable classification accuracy to detect network anomaly in a semi-supervised fashion even with incomplet”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, background): “In [13], Ugo et al. used discriminative RBM (DRBM) to develop a semi-supervised learning based network anomaly detection and evaluated its performance in an environment where network traﬃc for training and test scenarios were diﬀerent.”
Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “[31] developed a network anomaly detection system using discriminative RBM in conjunction with generative models with good classification accuracy abilities to gather knowledge from training data.”